A satellite-observed climatology of global temporal autocorrelations can be related to aerosol lifetimes

Schutgens, Nick; Andrews, Elisabeth J.; Arola, Antti; Bhatti, Yusuf; Fu, Guanliang; Hasekamp, Otto; Kolmonen, Pekka; Lipponen, Antti; Mielonen, Tero; Reygare, Leighton; Sayer, Andrew M.

doi:10.5194/egusphere-2026-2656

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https://doi.org/10.5194/egusphere-2026-2656

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29 May 2026

| 29 May 2026

Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

A satellite-observed climatology of global temporal autocorrelations can be related to aerosol lifetimes

Nick Schutgens, Elisabeth J. Andrews, Antti Arola, Yusuf Bhatti, Guanliang Fu, Otto Hasekamp, Pekka Kolmonen, Antti Lipponen, Tero Mielonen, Leighton Reygare, and Andrew M. Sayer

Abstract. Temporal autocorrelations of aerosol are often reported, but poorly understood. We use simple box models, Perturbed Parameters Ensembles of global aerosol models, AEROCOM (AEROsol Comparison of Observations and Models) simulations as well as satellite and AERONET (AErosol RObotic NETwork) observations to study temporal autocorrelations in aerosol optical depth (AOD). In particular, we present the first global climatology of observed temporal autocorrelations.

We develop a conceptual model for autocorrelations and relate them to important timescales, in particular lifetimes. We identify aerosol processes that affect autocorrelations and find autocorrelations provide information independent from yearly AOD, in particular on deposition processes. It is possible to estimate temporal autocorrelations in AOD from satellite observations by sensors like MODIS (MODerate resolution Imaging Spectroradiometer) or POLDER (POLarization and Directionality of the Earth’s Reflectances).

In our unique global climatology of observed temporal autocorrelations, regional variation is significant. Over remote oceans, the autocorrelation after 6 days tends to be low (∼ 0.2 or lower) but it is quite high in tropical outflow regions (∼ 0.5). Over land, it varies considerably, from 0.2 to 0.7. These spatial variations are much larger than observed year-to-year variation.

AEROCOM models often significantly overestimate autocorrelations. This suggests that loss processes are underestimated and/or contributions from seasonal sources are overestimated, which should have a marked impact on aerosol forcing estimates. Autocorrelations offer a new way to understand aerosol processes and evaluate models. Autocorrelations can be derived from existing observational datasets, for example surface black carbon mass concentrations or cloud condensation nuclei.

Received: 08 May 2026 – Discussion started: 29 May 2026

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Nick Schutgens, Elisabeth J. Andrews, Antti Arola, Yusuf Bhatti, Guanliang Fu, Otto Hasekamp, Pekka Kolmonen, Antti Lipponen, Tero Mielonen, Leighton Reygare, and Andrew M. Sayer

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RC1:
'Comment on egusphere-2026-2656', Anonymous Referee #1, 29 Jun 2026 reply
The study by Schutgens et al. is based on the temporal autocorrelation of AOD (a proxy for aerosol lifetime under certain conditions) and the key drivers of these autocorrelations. The authors provide a theoretical explanation of the relationship between the autocorrelation timescale and lifetime. By comparing temporal AOD autocorrelations from several satellite products with AERONET observations, they demonstrate that satellite snapshot data can be a useful tool for model evaluation. They then assess the performance of AEROCOM model simulations in reproducing AOD autocorrelations using satellite-based estimates. I find the methodology to be sound, and the uncertainties and limitations are discussed and quantified adequately. I have only a few minor comments aimed at improving the clarity of the manuscript. I recommend acceptance after the following comments have been addressed.
It is not mentioned how the fractional variance (shown in Fig. 4) is calculated for AOD and, more importantly, for the temporal autocorrelation at a lag of 6 days (ρ₆). It would also be useful to show how much variance (in absolute terms) is explained collectively by all of the contributing components for AOD, perhaps in a supplementary figure.

Fig 4a: The legend for EMI_DMS is not accurate.

Lines 252-253: It is surprising to see sea salt and DMS contributing substantially to the AOD variance over a predominantly continental region such as Australia. Could the authors comment on the physical reasons for this pattern?

Lines 247-249: The statement, "The first thing to notice is that the same processes are important for AOD and ρ₆ variance, although their relative contributions can be quite different" appears to contradict the discussion that follows, where the dominant contributors to AOD variance and ρ₆ variance differ in most regions. This statement seems more appropriate for the UKESM ensemble results shown in Figure 6.

Figure 8: It is not clear how the different lag values given in the caption should be interpreted, since this figure compares AOD rather than autocorrelation. In addition, the mean values reported in the legend of each panel appear to be significantly lower than suggested by the scatter points. Please verify these values. The same issue with mean values appears to be present in Figures 9 and 11.

Figure 8: Please state explicitly in the figure caption that the panels show comparisons for different satellite products. It would also be helpful to mention that the satellite products are based on 1° × 1° averages located close to or overlapping the AERONET sites. Also, please provide a list of the AERONET stations used in these comparisons in the supplementary.

The description of the AERONET sampling used to compute the autocorrelations shown in Figure 9 is missing. Were only the collocated AERONET observations corresponding to the satellite overpass times used (i.e., approximately one observation per day)? If so, the AERONET-derived autocorrelation coefficients may depend on the satellite overpass time and could therefore differ among the various panels of Figure 9. Please clarify.

Lines 349-354: Is the information discussed here presented elsewhere, for example in a supplementary figure or table? If so, please provide the appropriate reference.

Section 8: A useful addition would be a direct comparison of AOD between the models and the satellite products. Since the manuscript already discusses which processes dominate the variance in modelled AOD (Figures 4 and 6), such a comparison would help explain the differences in the estimated autocorrelations between the models and observations.

Lines 440-445: Given the discussion in Section 2.6, could the authors comment more explicitly on how the inferred autocorrelation timescales relate to aerosol lifetimes? Establishing this connection would help strengthen the physical interpretation of the results.

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Citation: https://doi.org/10.5194/egusphere-2026-2656-RC1

Nick Schutgens, Elisabeth J. Andrews, Antti Arola, Yusuf Bhatti, Guanliang Fu, Otto Hasekamp, Pekka Kolmonen, Antti Lipponen, Tero Mielonen, Leighton Reygare, and Andrew M. Sayer

Supplement

https://doi.org/10.5194/egusphere-2026-2656-supplement

Nick Schutgens, Elisabeth J. Andrews, Antti Arola, Yusuf Bhatti, Guanliang Fu, Otto Hasekamp, Pekka Kolmonen, Antti Lipponen, Tero Mielonen, Leighton Reygare, and Andrew M. Sayer

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Short summary

Aerosol are fine particles in the air that affect public health and the climate. We develop a new method to study aerosol through patterns (called autocorrelations) in their time-series. We show that autocorrelations are linked to aerosol deposition processes and contain information on the important but uncertain aerosol lifetime. Autocorrelations can be observed from satellites and we provide the first ever global climatology of aerosol autocorrelations.


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